Fuel-Optimal Rocket Landing with Aerodynamic Controls

Liu, Xinfu

doi:10.2514/1.g003537

Cited by 95 publications

(38 citation statements)

References 39 publications

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“…(20) is repeated until the sequence of trajectories converges. The nominal trajectory x (w−1) n,k+1 , ∀k for the (w)-th SCPn is obtained by integrating (20). The convergence of SCPn solutions to an optimal solution is proven in Theorem 2 by showing that the costs in subsequent iterations are non-increasing and convergent.…”

Section: Sequential Convex Programming With Nonlinear Dynamics Conmentioning

confidence: 99%

“…SCP has also been used by roboticists for problems like team-based path planning in non-convex environments [18] and complex robot, complex environment optimal path planning [19]. Recently, SCP has been applied to tougher problems with tight constraints like optimal entry and landing [20][21][22]. Often, the optimal state trajectory found with SCP is implemented with a tracking controller rather than determining the optimal control concurrently with the optimal state [23].…”

Section: Introductionmentioning

confidence: 99%

“…Simulations used CVX, a MATLAB-based convex optimization engine running the Mosek solver[40][41][42]. The nonlinear trajectories were found by numerically integrating the nonlinear dynamics, as seen in Eqs (12),(20). using the optimal control trajectory found using SCP, SCPn, and M-SCPn.…”

mentioning

confidence: 99%

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Optimal Guidance and Control with Nonlinear Dynamics Using Sequential Convex Programming

Foust

Chung

Hadaegh

2020

Journal of Guidance, Control, and Dynamics

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This paper presents a novel method for expanding the use of sequential convex programming (SCP) to the domain of optimal guidance and control problems with nonlinear dynamics constraints. SCP is a useful tool in obtaining real-time solutions to direct optimal control, but it is unable to adequately model nonlinear dynamics due to the linearization and discretization required. As nonlinear program solvers are not yet functioning in real-time, a tool is needed to bridge the gap between satisfying the nonlinear dynamics and completing execution fast enough to be useful. Two methods are proposed, sequential convex programming with nonlinear dynamics correction (SCPn) and modified SCPn (M-SCPn), which mixes SCP and SCPn to reduce runtime and improve algorithmic robustness. Both methods are proven to generate

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Section: Sequential Convex Programming With Nonlinear Dynamics Conmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Guidance and Control with Nonlinear Dynamics Using Sequential Convex Programming

Foust

Chung

Hadaegh

2020

Journal of Guidance, Control, and Dynamics

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“…subject to Eqs. (27), (16), (14) in discrete form, together with the conic constraint u 2 ≤ σ. The initial state is known, and the initial control is extrapolated from the control history once that the problem is solved.…”

Section: F Constraintsmentioning

confidence: 99%

Generalized hp Pseudospectral-Convex Programming for Powered Descent and Landing

Sagliano

2019

Journal of Guidance, Control, and Dynamics

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“…Reusable rocket is becoming an important space vehicle due to its low launch cost [1]. One of the key technologies to make rockets practically reusable is called as precision landing [2]. To achieve precision landing under large initial state deviation, the landing trajectory should be replanned in real time when new information is obtained in flight.…”

Section: Introductionmentioning

confidence: 99%

A Convex Programming Method for Rocket Powered Landing With Angle of Attack Constraint

et al. 2020

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Real time trajectory planning is vital in rocket precision powered landing guidance. Due to the nonconvex angle of attack (AOA) constraint and other constraints, including nonlinear dynamics and thrust constraint, the powered landing trajectory planning problem is highly nonconvex, which makes it difficult to be solved in real time via existing nonconvex optimization algorithms. As the main contribution of this paper, the AOA constraint is taken into account in the problem. A convex feasible set method is presented to handle it, in which a quadratic concave function is introduced to find a convex feasible subset in the original AOA constraint and the second order term of this function is estimated by Gersgorin disc theorem. For the remaining nonconvexities, the lossless convexification is employed to address the thrust constraint, and the successive linearization is performed to handle the nonlinear dynamics. Thus, a convex optimization problem with AOA constraint for landing trajectory planning is built. The optimal solution to the original problem is obtained by iteratively solving convex problems. Numerical simulations show that the proposed method can find a feasible landing trajectory where the AOA constraint is satisfied and has a better convergence performance compared with the traditional linearization method. INDEX TERMS Convex feasible set, angle of attack constraint, powered landing.

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Fuel-Optimal Rocket Landing with Aerodynamic Controls

Cited by 95 publications

References 39 publications

Optimal Guidance and Control with Nonlinear Dynamics Using Sequential Convex Programming

Optimal Guidance and Control with Nonlinear Dynamics Using Sequential Convex Programming

Generalized hp Pseudospectral-Convex Programming for Powered Descent and Landing

A Convex Programming Method for Rocket Powered Landing With Angle of Attack Constraint

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